CN114412773B - Intelligent diagnosis system for machine pump group faults - Google Patents
Intelligent diagnosis system for machine pump group faults Download PDFInfo
- Publication number
- CN114412773B CN114412773B CN202210054796.3A CN202210054796A CN114412773B CN 114412773 B CN114412773 B CN 114412773B CN 202210054796 A CN202210054796 A CN 202210054796A CN 114412773 B CN114412773 B CN 114412773B
- Authority
- CN
- China
- Prior art keywords
- fault
- phenomenon
- value
- judging
- reasons
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 45
- 230000002159 abnormal effect Effects 0.000 claims description 38
- 238000006073 displacement reaction Methods 0.000 claims description 24
- 239000007788 liquid Substances 0.000 claims description 14
- 230000005856 abnormality Effects 0.000 claims description 13
- 238000000034 method Methods 0.000 claims description 12
- 230000001133 acceleration Effects 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 5
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 238000010521 absorption reaction Methods 0.000 claims description 2
- 238000005461 lubrication Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 6
- 239000010687 lubricating oil Substances 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 230000002265 prevention Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000005213 imbibition Methods 0.000 description 3
- 238000013021 overheating Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000007789 sealing Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000008235 industrial water Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B51/00—Testing machines, pumps, or pumping installations
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses an intelligent diagnosis system for machine pump group faults, which comprises a database, a control module and a control module, wherein the database stores a fault phenomenon-fault reason lookup table; the acquisition module is used for detecting the operation parameters of the machine pump; a fault diagnosis module for: and acquiring a fault phenomenon according to the operation parameters, and searching a fault reason from the fault phenomenon-fault reason lookup table. The system can diagnose faults quickly and timely, can find out and prevent faults in advance, and better ensures stable operation of equipment.
Description
Technical Field
The invention belongs to the technical field of automatic fault diagnosis, and particularly relates to an intelligent fault diagnosis system for a machine pump group.
Background
Machine pumps are used in large quantities in the manufacturing industry, and the normal operation of the machine pumps has a considerable significance for the normal operation of the equipment.
The fault treatment of the pump is often carried out after the problem occurs, and the process occupies more time and seriously affects the production efficiency of enterprises.
The later industrial water pump fault diagnosis technology is based on data signal acquisition of vibration temperature sensors in various different communication modes, various feature vectors in a system time domain and a system frequency domain are obtained by utilizing a signal analysis theory, and the position of a fault source is judged by utilizing the relation between the feature vectors and the system fault source, so that fault early warning and fault diagnosis are carried out. The existing water pump fault diagnosis algorithm mainly utilizes BP neural network to model the nonlinear relation between each operation characteristic value of the water pump and fault type, and predicts faults. The disadvantage is that enough learning samples are required to ensure the reliability of the diagnosis. When diagnosing a complex system, the problems of overlarge network scale, too long learning and training time and the like are caused, and the complex system has certain hysteresis. When the device is used in an industrial field, the device can not be fed back timely or diagnosed inaccurately, so that the purpose cannot be achieved, the effect of early prevention cannot be mentioned, the stable operation of the device cannot be guaranteed better, and the service life of the device cannot be prolonged.
The above information disclosed in this background section is only for enhancement of understanding of the background section of the application and therefore it may not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
Aiming at the technical problems of complex diagnosis mode and hysteresis of a water pump fault diagnosis system in the prior art, the invention provides an intelligent diagnosis system for machine pump group faults, which can solve the problems.
In order to achieve the aim of the invention, the invention is realized by adopting the following technical scheme:
an intelligent diagnostic system for machine pump group faults, comprising:
A database storing a fault phenomenon-fault cause lookup table;
the acquisition module is used for detecting the operation parameters of the machine pump;
A fault diagnosis module for:
And acquiring a fault phenomenon according to the operation parameters, and searching a fault reason from the fault phenomenon-fault reason lookup table.
The database also stores a fault reason-fault phenomenon corresponding table and a fault phenomenon-fault reason elimination table which does not show the fault phenomenon;
the diagnostic system further includes a collection processing module for building a fault phenomenon-fault cause lookup table, comprising:
Determining a plurality of fault reasons and a plurality of fault phenomena, and respectively obtaining the representation states of the fault phenomena when the fault reasons occur, wherein the representation comprises representation and non-representation, a corresponding table of the fault phenomena with the represented fault reasons and states is established, a corresponding table of the fault phenomena with the non-represented fault reasons and states is established, and a corresponding table of the fault phenomena with the non-represented fault reasons and states is established;
Acquiring the represented fault phenomenon, and acquiring all possible fault reasons from a fault reason-fault phenomenon corresponding table to obtain primary detection fault reasons;
obtaining a failure phenomenon which is not represented, and obtaining failure reasons which can be removed from a failure phenomenon-failure reason removal table which is not represented;
And removing the fault reasons which can be removed from the primary detection fault reasons, and obtaining a fault phenomenon-fault reason lookup table.
Further, the fault phenomenon corresponds to an operation parameter, and the performance state judging method of the fault phenomenon comprises the following steps:
Detecting each operation parameter;
and comparing each operation parameter with the corresponding set threshold value, and judging that the state of the fault phenomenon corresponding to the operation parameter is represented when the threshold value range is exceeded, or else, judging that the state is not represented.
Furthermore, the diagnosis system further comprises a display output module, the fault reasons also correspond to solution information, and after the final fault reasons are obtained, the diagnosis system further comprises the steps of obtaining the solution information corresponding to the final fault reasons and displaying and outputting the solution information through the display output module.
Further, the acquisition module includes:
The flow acquisition module is used for detecting an outlet flow value X1 of the equipment;
the fault diagnosis module judges displacement abnormality according to the outlet flow value, and comprises the following steps:
acquiring a rated flow value Q1 and an operation point flow value Q0;
When X1 is more than or equal to (1+a1) Q1 or X1 is less than or equal to (1-b 1) Q1, judging that the displacement is abnormal;
when (1+a2) Q0 < X1 < (1+a1) Q1, judging that the displacement is higher;
when (1-b 1) Q1 < X1 < (1-b 2) Q0, judging that the displacement is lower;
wherein a2 is more than 0 and a1 is more than 1, b2 is more than 0 and b1 is more than 1.
Further, the acquisition module includes:
the current acquisition module is used for detecting an operation current value I0 of the equipment;
the fault diagnosis module judges abnormal liquid absorption according to the running current value I0 and the outlet flow value X1, and comprises the following steps:
when I0 > 0 and q=0, it is judged that the liquid suction is abnormal.
Further, the fault diagnosis module further comprises a step of judging power consumption abnormality according to the operation current value I0 and the average operation current value I1:
obtaining an average running current value I1;
when I0 is more than or equal to (1+a3) I1, judging that the work consumption is abnormal;
wherein 0 < a3 < 1.
Further, the acquisition module includes:
A vibration sensor for detecting a vibration speed and a vibration acceleration of the apparatus;
the fault diagnosis module further includes a step of judging vibration abnormality according to the vibration speed and the vibration acceleration:
When the vibration speed or the vibration acceleration exceeds a set threshold value, it is determined that the vibration is abnormal.
Further, the acquisition module includes:
a pressure sensor for detecting an outlet pressure value X3 of the apparatus;
the fault diagnosis module further comprises means for judging that the outlet pressure is abnormal according to the outlet pressure value X3:
Acquiring an operating point pressure value P0;
judging that the outlet pressure is abnormal when X3 is less than P0 (1-a 4) or X3 is more than P0 (1+a4);
Wherein 0 < a4 < 1.
Further, the acquisition module further includes:
A temperature sensor for detecting a temperature value X4 of the pump body;
The fault diagnosis module further comprises a step of judging that the temperature of the pump body is abnormal according to the temperature value X4:
Acquiring a threshold T0 of the pump body;
When X4 is more than T0+a5, judging that the temperature of the pump body is abnormal, wherein 0 < a5 < 15.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the intelligent diagnosis system for the machine pump group faults, through obtaining the represented fault phenomena, all possible fault reasons are obtained from a fault reason-fault phenomenon correspondence table, and primary detection fault reasons are obtained; obtaining a failure phenomenon which is not represented, and obtaining failure reasons which can be removed from a failure phenomenon-failure reason removal table which is not represented; the failure reasons which can be eliminated are eliminated from the primary failure reasons to obtain final failure reasons, and the method is rapid and timely in failure diagnosis, can achieve the effects of early discovery and early prevention, and better ensures the stable operation of equipment.
Other features and advantages of the present invention will become apparent upon review of the detailed description of the invention in conjunction with the drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of one embodiment of a machine pump group fault intelligent diagnostic system in accordance with the present invention;
FIG. 2 is a fault cause-fault phenomenon lookup table in one embodiment of a pump group fault intelligent diagnostic system.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, in the description of the present invention, terms such as "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus are not to be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Example 1
The embodiment provides an intelligent diagnosis system for machine pump group faults, as shown in fig. 1, including:
A database storing a fault phenomenon-fault cause lookup table;
the acquisition module is used for detecting the operation parameters of the machine pump;
A fault diagnosis module for:
And acquiring a fault phenomenon according to the operation parameters, and searching a fault reason from the fault phenomenon-fault reason lookup table.
The database is also stored with a fault reason-fault phenomenon corresponding table and a fault phenomenon-fault reason eliminating table;
The diagnostic system further includes a collection processing module for building a fault phenomenon-fault cause lookup table, comprising:
Determining a plurality of fault reasons and a plurality of fault phenomena, and respectively obtaining the representation states of the fault phenomena when the fault reasons occur, wherein the representation comprises representation and non-representation, a corresponding table of the fault phenomena with the represented fault reasons and states is established, a corresponding table of the fault phenomena with the non-represented fault reasons and states is established, and a corresponding table of the fault phenomena with the non-represented fault reasons and states is established;
Acquiring the represented fault phenomenon, and acquiring all possible fault reasons from a fault reason-fault phenomenon corresponding table to obtain primary detection fault reasons;
obtaining a failure phenomenon which is not represented, and obtaining failure reasons which can be removed from a failure phenomenon-failure reason removal table which is not represented;
And removing the fault reasons which can be removed from the primary detection fault reasons, and obtaining a fault phenomenon-fault reason lookup table.
The collecting and processing module can collect parameter information of the equipment all the time, after the equipment fails, the parameter data of the equipment when the equipment fails are extracted, analysis and judgment are carried out on the data according to algorithm rules which are already set by the system, for example, a judgment result is expressed as normal 1 in a binary mode and is abnormal. And locking the fault phenomenon according to the result to obtain a fault reason list L1, obtaining an impossible fault reason list L2 according to the fault phenomenon which does not occur, removing the reason from the L1 as long as the reason in the L1 appears in the L2, and finally reporting the result to a device manager for the rest of the L1 as the device fault reason, and prompting corresponding hazard results and solutions for the fault reason.
As shown in fig. 2, is a fault phenomenon-fault cause lookup table established by the above method. The first row of the lookup table records a plurality of fault phenomena, the first column records fault reasons, and the representation states of the fault phenomena and the fault reasons are respectively represented by different numerical values. In this embodiment, 0 is represented as normal and 1 is represented as abnormal in binary. For example, when the pump body is overheated (with a value of 1) and does not go out of the liquid (with a value of 1) in the detected fault phenomena, and other fault phenomena are normal (with a value of 0), the fault source which can be found through the lookup table is not pumping.
When the detected fault phenomenon is low in displacement (value of 1), imbibition is not carried out after starting (value of 1), vibration is large (value of 1) and liquid is not carried out (value of 1), and other fault phenomena are normal (value of 0), the fault source which can be found through the lookup table is incomplete liquid filling.
As described in the lookup table, the corresponding failure cause may be also found and determined according to various failure phenomenon combinations, which are not listed here.
The system acquires parameter data of the equipment from an acquisition module or other systems, and stores the parameter data into an InfluxDB database and a Redis database; obtaining the latest parameter information of the equipment from the redis to carry out diagnosis logic processing; the parameter information must be obtained by obtaining parameter data in the case of normal operation of the device.
According to the intelligent diagnosis method for the machine pump group faults, through obtaining the represented fault phenomena, all possible fault reasons are obtained from a fault reason-fault phenomenon correspondence table, and primary detection fault reasons are obtained; obtaining a failure phenomenon which is not represented, and obtaining failure reasons which can be removed from a failure phenomenon-failure reason removal table which is not represented; the failure reasons which can be eliminated are eliminated from the primary failure reasons to obtain final failure reasons, and the method is rapid and timely in failure diagnosis, can achieve the effects of early discovery and early prevention, and better ensures the stable operation of equipment.
The diagnosis system also comprises a display output module, the fault reasons also correspond to solution information, and after the final fault reasons are obtained, the diagnosis system also comprises the steps of obtaining the solution information corresponding to the final fault reasons, displaying and outputting through the display output module, so that the equipment manager is conveniently prompted.
The preferable fault reasons also correspond to solution information, and after the final fault reason is obtained, the method further comprises the steps of obtaining the solution information corresponding to the final fault reason and displaying and outputting.
The fault phenomenon corresponds to the operation parameters, and the performance state judging method of the fault phenomenon comprises the following steps:
Detecting each operation parameter;
And comparing each operation parameter with the corresponding set threshold value, and judging the state of the fault phenomenon corresponding to the operation parameter to be represented when the threshold value range is exceeded, or else, judging the state to be unrepresented.
Judging the equipment parameter data through the diagnosis logic can obtain whether the current running state of the equipment is normal or not, and generating corresponding fault and solution information.
The fault phenomenon comprises any combination of abnormal displacement, abnormal outlet pressure, abnormal liquid suction, abnormal power consumption, abnormal vibration, overheated pump body, overheated bearing box, abnormal liquid outlet, sealing leakage fault, abnormal sealing life and abnormal bearing life.
In this embodiment, the acquisition module includes:
The flow acquisition module is used for detecting an outlet flow value X1 of the equipment;
the fault diagnosis module judges displacement abnormality according to the outlet flow value, and comprises the following steps:
acquiring a rated flow value Q1 and an operation point flow value Q0;
When X1 is more than or equal to (1+a1) Q1 or X1 is less than or equal to (1-b 1) Q1, judging that the displacement is abnormal;
when (1+a2) Q0 < X1 < (1+a1) Q1, judging that the displacement is higher;
when (1-b 1) Q1 < X1 < (1-b 2) Q0, judging that the displacement is lower;
wherein a2 is more than 0 and a1 is more than 1, b2 is more than 0 and b1 is more than 1.
In industrial production, the outlet flow value of the device is monitored, and when the monitored outlet flow value is lower than a certain range, the device displacement is considered to be low.
The operating point flow value Q0 is taken from the DCS system, and the rated flow value Q1 is taken from the database.
For example, as shown in Table 1, the device is proved to be in a normal state when the acquired outlet flow value X1 of the device is between 90% and 110% of Q0; the device is considered to be low in displacement but does not affect the operation of the device when X1 is between 90% of Q0 and 40% of Q1, and low in displacement and fails when X1 is less than 90% of Q0 and less than 40% of Q1, which affects the normal operation of the device; when X1 is more than 110% of Q0 and less than 120% of Q1, the large displacement abnormality does not affect the operation of the equipment; when X1 is greater than 110% of Q0 and greater than 120% of Q1, displacement failure can affect proper operation of the device.
Normal: q0 is more than or equal to 90 percent and less than or equal to X1 is more than or equal to 110 percent;
The displacement is low: Q1X 40% < X1< Q0X 90%;
displacement low failure: x1< Q1X 40%;
high discharge: Q0X 110% < X1< Q1X 120%;
High displacement failure: q1 is 120% < X1.
TABLE 1
When the collected outlet flow value X1 of the equipment is 90% of Q0, the equipment is considered to have low discharge capacity but cannot influence the operation of the equipment, and when X1 is less than 90% of Q0 and less than 40% of Q1, the discharge capacity of the equipment is low and fails, and the normal operation of the equipment is influenced at the moment; when X1 is more than 110% of Q0 and less than 120% of Q1, the large displacement abnormality does not affect the operation of the equipment; when X1 is greater than 110% of Q0 and greater than 120% of Q1, displacement failure can affect proper operation of the device.
The acquisition module of the present embodiment further includes a current acquisition module for detecting an operation current value I0 of the apparatus.
The operation parameters corresponding to the imbibition abnormality comprise an operation current value I0 and a flow value Q of equipment, and the fault diagnosis module judges the imbibition abnormality according to the operation current value I0 and an outlet flow value X1 and comprises the following steps:
when I0 > 0 and q=0, it is judged that the liquid suction is abnormal.
The fault diagnosis module further comprises a step of judging abnormal power consumption according to the operation current value I0 and the average operation current value I1:
obtaining an average running current value I1;
when I0 is more than or equal to (1+a3) I1, judging that the work consumption is abnormal;
wherein 0 < a3 < 1.
The equipment power consumption is based on the current value of the equipment, and when the monitored current value of the equipment is larger than the upper limit current value set by the equipment, the equipment is considered to be in fault, and the fault phenomenon is the power consumption; because the fluctuation of the current value is relatively large, the current standard value of the equipment is obtained by statistics of historical data generated in the normal state of the equipment, namely the minimum current value of the equipment in the same flow in the normal state.
The average current value of the I1 historical data is taken from a power substation;
i0 is the latest current value of the current device.
Setting a standard value and a default range when the system is initially deployed, then taking an average value according to the data collected by the system for two to three days, and setting, wherein the voltage is considered to be unchanged when the current value system is judged; when the current value of the monitoring equipment is larger than 104% of the standard point value, the equipment is considered to have a fault with high power consumption.
For example: i0 And judging that the power consumption is abnormal when the power consumption is more than or equal to 104% of I1.
The operation parameters corresponding to the vibration abnormality are the vibration speed and the vibration acceleration of the device, and therefore, the acquisition module of the embodiment includes a vibration sensor for detecting the vibration speed and the vibration acceleration of the device; the fault diagnosis module further includes a step of judging vibration abnormality according to the vibration speed and the vibration acceleration, including:
When the vibration speed or the vibration acceleration exceeds a set threshold value, it is determined that the vibration is abnormal.
When the vibration amplitude is unqualified or the variation exceeds 25% of the alarm value in the stable operation of the unit, the equipment fails; the vibration limit value of the machine set at the starting and critical rotation speed is that the specified shaft vibration is not more than 260 mu m, the bearing seat vibration is not more than 100 mu m, and other faults are caused.
The system defaults to failure when the vibration value of the device does not meet the range specified by the standard; the allowable range of vibration when the system is actually deployed needs to be set according to the actual situation and the user advice.
The equipment in production has outlet pressure values which are required to be met, if the outlet pressure value of a certain equipment is monitored to change in the actual production process, the pressure change value obtained through analysis exceeds the fluctuation range allowed in the actual production, and the equipment has a fault of low outlet pressure.
The acquisition module of the present embodiment further comprises a pressure sensor for detecting an outlet pressure value X3 of the device.
The fault diagnosis module further comprises means for judging that the outlet pressure is abnormal according to the outlet pressure value X3:
the pressure value P0 of the operating point is obtained, and the pressure value P0 is taken from the DCS system.
Judging that the outlet pressure is abnormal when X3 is less than P0 (1-a 4) or X3 is more than P0 (1+a4);
Wherein 0 < a4 < 1.
For example, when the collected data X3 is in a range outside the positive and negative 3% of the operation point value, it is confirmed that the apparatus has failed; namely, when X3 is less than P0X 97% or X3 is more than P0X 103%, the equipment is in fault; 3% is a default range value set by the system for the outlet pressure value, and in actual deployment of the system, the range value can be reset according to the outlet pressure value and the fluctuation range which the user provides under the actual running condition of the equipment.
The temperature standard value of the pump body is derived from the temperature data of the pump body collected in a normal state after the system is deployed, the average value of the temperatures in a period of time is taken as the standard value, when the temperature of pump equipment in production is higher than the upper limit value of the set temperature, the pump fails, and the failure phenomenon is that the pump is overheated.
The acquisition module further comprises a temperature sensor, which is used for detecting a temperature value X4 of the pump body.
The fault diagnosis module further comprises a step of judging that the temperature of the pump body is abnormal according to the temperature value X4:
A threshold T0 of the pump body is obtained.
T0 is the average of the operating point temperature values from the data collected by the first deployment system.
When X4 is more than T0+a5, judging that the temperature of the pump body is abnormal, wherein 0 < a5 < 15.
For example, when the monitored temperature of the device is 10 ℃ higher than the set standard value, it indicates that the temperature of the device is too high.
That is, when X4> t0+10, it is determined that the pump body is overheated.
According to different equipment types, the judging basis of overheating of the bearing box is different; a fault is caused when the temperature rise of the lubricating oil for forced lubrication is larger than a set range value; the bearing box temperature of the non-forced lubrication is greater than the set upper limit value and is a fault; the failure phenomenon is overheating of the bearing housing.
The theta is the temperature rise and refers to the difference between the temperature when the lubricating oil enters the bearing box and the temperature when the lubricating oil exits the bearing box at the same time; t01 is the temperature value of the current monitoring bearing box; t11 is the temperature when the lubricating oil for forced lubrication enters the bearing box; t12 temperature at which the forced lubrication oil exits the bearing housing.
The forced lubrication fails when the temperature rise of the lubricating oil is more than 28 ℃; the bearing box with non-forced lubrication is in fault when the temperature is higher than 93 ℃; the failure phenomenon is overheating of the bearing housing.
Namely: when Θ = T12-T11; and judging that the bearing box has overheat fault when theta is more than 28 ℃.
And judging that the bearing box has overheat fault when T01 is higher than 93 ℃.
For the failure without liquid, after one device is started for a period of time, if the flow value of the monitored device is always 0, the device is considered to be failed, and the failure phenomenon is that the liquid is not discharged.
Q is the latest outlet flow value and is taken from the DCS system;
I is that the latest current is taken from a substation or a transformer;
t is 30 seconds from the current greater than 0.
When the current value of the equipment is monitored to be changed from 0 to be more than 0, the equipment is considered to be changed from a deactivated state to an activated state, the current data and the flow data of the equipment are monitored after the equipment is activated, when the current is more than 0 and the flow value is 0, the parameters of the equipment are monitored and obtained after 30 seconds, the equipment is considered to be faulty, and the fault phenomenon is not out of liquid.
When I >0and q=0 and t >30, it is judged that no liquid is out of order.
The fault reasons also correspond to hazard reminding information, and after the final fault reasons are obtained, the method further comprises the steps of obtaining hazard reminding information corresponding to the final fault reasons and displaying and outputting the hazard reminding information. By reporting the final fault cause to the equipment manager, and prompting corresponding hazard results and solutions for the fault cause.
The method reduces the range of fault reasons by screening and filtering, prompts what harm the fault can cause, and helps factories to rapidly locate the fault reasons and provide corresponding solutions. The elimination method is gradually perfected by a big data self-learning mode, so that the system achieves the effects of early discovery and early prevention, and the stable operation of equipment is better ensured.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. An intelligent diagnostic system for machine pump group faults, which is characterized by comprising:
A database storing a fault phenomenon-fault cause lookup table;
the acquisition module is used for detecting the operation parameters of the machine pump;
A fault diagnosis module for:
acquiring a fault phenomenon according to the operation parameters, and searching a fault reason from the fault phenomenon-fault reason lookup table;
the database also stores a fault reason-fault phenomenon corresponding table and a fault phenomenon-fault reason elimination table which does not show the fault phenomenon;
the diagnostic system further includes a collection processing module for building a fault phenomenon-fault cause lookup table, comprising:
Determining a plurality of fault reasons and a plurality of fault phenomena, and respectively acquiring the states of the fault phenomena when the fault reasons occur, wherein the states comprise represented and unrepresented, establishing a corresponding table of the fault reasons and the fault phenomena with the represented states, which is a fault reason-fault phenomenon corresponding table, establishing a corresponding table of the fault reasons and the fault phenomena with the unrepresented states, which is a fault phenomenon-fault reason removing table;
Acquiring fault phenomena with the state being the expression, and acquiring all possible fault reasons from a fault reason-fault phenomenon corresponding table to obtain primary detection fault reasons;
acquiring fault phenomena with the state being unrepresented, and acquiring fault reasons which can be removed from a fault phenomenon unrepresented-fault reason removal table;
And removing the fault reasons which can be removed from the primary detection fault reasons, and obtaining a fault phenomenon-fault reason lookup table.
2. The intelligent diagnostic system for a pump group fault of claim 1, wherein the fault phenomenon corresponds to an operation parameter, and the state determining method of the fault phenomenon is as follows:
Detecting each operation parameter;
and comparing each operation parameter with the corresponding set threshold value, and judging that the state of the fault phenomenon corresponding to the operation parameter is represented when the threshold value range is exceeded, or else, judging that the state is not represented.
3. The intelligent diagnosis system for failure of a pump group according to claim 1, further comprising a display output module, wherein the failure cause corresponds to solution information, and further comprising obtaining solution information corresponding to the final failure cause after obtaining the final failure cause, and displaying and outputting the solution information through the display output module.
4. The intelligent diagnostic system of a pump group fault of claim 1, wherein the acquisition module comprises:
The flow acquisition module is used for detecting an outlet flow value X1 of the equipment;
the fault diagnosis module judges displacement abnormality according to the outlet flow value, and comprises the following steps:
acquiring a rated flow value Q1 and an operation point flow value Q0;
when X1 is more than or equal to (1+a1) Q1 or X1 is less than or equal to (1-b 1) Q1, judging that the displacement is abnormal;
When (1+a2) Q0 < X1 < (1+a1) Q1, judging that the displacement is higher;
when (1-b 1) Q1 < X1 < (1-b 2) Q0, judging that the displacement is lower;
wherein a2 is more than 0 and a1 is more than 1, b2 is more than 0 and b1 is more than 1.
5. The intelligent diagnostic system of a pump farm fault according to claim 4, wherein the acquisition module comprises:
the current acquisition module is used for detecting an operation current value I0 of the equipment;
the fault diagnosis module judges abnormal liquid absorption according to the running current value I0 and the outlet flow value X1, and comprises the following steps:
When I0 > 0 and q=0, it is judged that the liquid suction is abnormal.
6. The intelligent diagnostic system for a pump group fault of claim 5, wherein the fault diagnostic module further comprises means for determining a power consumption abnormality from the operating current value I0 and an average operating current value I1:
obtaining an average running current value I1;
When I0 is more than or equal to (1+a3) I1, judging that the work consumption is abnormal;
wherein 0 < a3 < 1.
7. The intelligent diagnostic system of a pump group fault of claim 1, wherein the acquisition module comprises:
A vibration sensor for detecting a vibration speed and a vibration acceleration of the apparatus;
the fault diagnosis module further includes a step of judging vibration abnormality according to the vibration speed and the vibration acceleration:
When the vibration speed or the vibration acceleration exceeds a set threshold value, it is determined that the vibration is abnormal.
8. The intelligent diagnostic system of a pump group fault of claim 1, wherein the acquisition module comprises:
a pressure sensor for detecting an outlet pressure value X3 of the apparatus;
the fault diagnosis module further comprises means for judging that the outlet pressure is abnormal according to the outlet pressure value X3:
Acquiring an operating point pressure value P0;
judging that the outlet pressure is abnormal when X3 is less than P0 (1-a 4) or X3 is more than P0 (1+a4);
Wherein 0 < a4 < 1.
9. The intelligent diagnostic system for a pump farm fault according to any of claims 1-8, wherein the acquisition module further comprises:
A temperature sensor for detecting a temperature value X4 of the pump body;
The fault diagnosis module further comprises a step of judging that the temperature of the pump body is abnormal according to the temperature value X4:
Acquiring a threshold T0 of the pump body;
When X4 is more than T0+a5, judging that the temperature of the pump body is abnormal, wherein 0 < a5 < 15.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210054796.3A CN114412773B (en) | 2022-01-18 | 2022-01-18 | Intelligent diagnosis system for machine pump group faults |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210054796.3A CN114412773B (en) | 2022-01-18 | 2022-01-18 | Intelligent diagnosis system for machine pump group faults |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114412773A CN114412773A (en) | 2022-04-29 |
| CN114412773B true CN114412773B (en) | 2024-05-28 |
Family
ID=81273928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210054796.3A Active CN114412773B (en) | 2022-01-18 | 2022-01-18 | Intelligent diagnosis system for machine pump group faults |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114412773B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101065767B1 (en) * | 2010-04-22 | 2011-09-19 | 주식회사 지오네트 | Online early fault detection and diagnostic method for plant operation |
| CN106761681A (en) * | 2017-02-16 | 2017-05-31 | 中国石油化工股份有限公司 | Electric pump well fault real-time diagnosis system and method based on time series data analysis |
| CN109538459A (en) * | 2018-10-17 | 2019-03-29 | 重庆川仪自动化股份有限公司 | Pump equipment fault monitoring operational system and method based on networking |
| KR102141391B1 (en) * | 2019-12-16 | 2020-08-05 | 주식회사 한국가스기술공사 | Failure data management method based on cluster estimation |
| CN113090615A (en) * | 2021-04-07 | 2021-07-09 | 哈尔滨理工大学 | Hydraulic oil source electronic control unit |
| KR102278199B1 (en) * | 2020-12-31 | 2021-07-16 | 주식회사 한국가스기술공사 | Method for managing diagnostic data based on conditional probability |
| CN113723632A (en) * | 2021-08-27 | 2021-11-30 | 北京邮电大学 | Industrial equipment fault diagnosis method based on knowledge graph |
-
2022
- 2022-01-18 CN CN202210054796.3A patent/CN114412773B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101065767B1 (en) * | 2010-04-22 | 2011-09-19 | 주식회사 지오네트 | Online early fault detection and diagnostic method for plant operation |
| CN106761681A (en) * | 2017-02-16 | 2017-05-31 | 中国石油化工股份有限公司 | Electric pump well fault real-time diagnosis system and method based on time series data analysis |
| CN109538459A (en) * | 2018-10-17 | 2019-03-29 | 重庆川仪自动化股份有限公司 | Pump equipment fault monitoring operational system and method based on networking |
| KR102141391B1 (en) * | 2019-12-16 | 2020-08-05 | 주식회사 한국가스기술공사 | Failure data management method based on cluster estimation |
| KR102278199B1 (en) * | 2020-12-31 | 2021-07-16 | 주식회사 한국가스기술공사 | Method for managing diagnostic data based on conditional probability |
| CN113090615A (en) * | 2021-04-07 | 2021-07-09 | 哈尔滨理工大学 | Hydraulic oil source electronic control unit |
| CN113723632A (en) * | 2021-08-27 | 2021-11-30 | 北京邮电大学 | Industrial equipment fault diagnosis method based on knowledge graph |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114412773A (en) | 2022-04-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110059325B (en) | Vehicle fault early warning system and corresponding vehicle fault early warning method | |
| CN104698321B (en) | The detecting system of electrical equipment failure | |
| CN103180564B (en) | For diagnosing the method and apparatus of the coolant pump for motor | |
| CN116435634B (en) | Storage battery temperature state monitoring and management system | |
| CN114412773B (en) | Intelligent diagnosis system for machine pump group faults | |
| CN204495934U (en) | The detection system of consumer fault | |
| US11746680B2 (en) | System for monitoring an engine | |
| CN113865001A (en) | Air conditioning unit control method and device and air conditioning unit | |
| CN108223401B (en) | Electric pump overload fault diagnosis method and device | |
| CN111198540B (en) | Equipment monitoring method and device | |
| CN107327346A (en) | Monitoring method, device and the engine controller of starter working life | |
| CN114417067A (en) | Intelligent diagnosis method for faults of pump group | |
| JP2024517383A (en) | Method, apparatus and fan for monitoring fan operation | |
| CN205785820U (en) | A kind of engine diagnosis platform | |
| CN210721070U (en) | Control system of medical CT bulb tube heat radiation oil pump | |
| CN110165969B (en) | Control device for machine tool | |
| CN110578705A (en) | Fault diagnosis method and device | |
| CN219496593U (en) | Intelligent motor monitoring control system | |
| CN114508391B (en) | Monitoring and early warning system and method for turbine lubricating oil system | |
| CN112682159B (en) | Fault diagnosis method for water pump, control method for engine and engine | |
| CN116025556A (en) | Winding fault diagnosis method and system for integrated pump station | |
| CN108757092B (en) | Motor vehicle safety control method based on filter | |
| CN108020406B (en) | Veneer reeling machine health status monitoring and early warning equipment | |
| CN217932003U (en) | Detection circuit of switching value sensor measuring circuit | |
| CN215866922U (en) | Fan fault detection circuit and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |